rm(list = ls())
graphics.off()
BADA
data <- read.csv("IBM-HR-Emplyee-NoAttrition.csv")
rownames(data) <- data$Subj
data1 <- data[sapply(data, function(x) !is.factor(x))]
data1 <- subset(data1, select=-Subj)
(library(Matrix))
## [1] "Matrix" "stats" "graphics" "grDevices" "utils" "datasets"
## [7] "methods" "base"
(library(prettyGraphs))
## [1] "prettyGraphs" "Matrix" "stats" "graphics"
## [5] "grDevices" "utils" "datasets" "methods"
## [9] "base"
(library(ExPosition))
## [1] "ExPosition" "prettyGraphs" "Matrix" "stats"
## [5] "graphics" "grDevices" "utils" "datasets"
## [9] "methods" "base"
(library(InPosition))
## [1] "InPosition" "ExPosition" "prettyGraphs" "Matrix"
## [5] "stats" "graphics" "grDevices" "utils"
## [9] "datasets" "methods" "base"
(library(TExPosition))
## [1] "TExPosition" "InPosition" "ExPosition" "prettyGraphs"
## [5] "Matrix" "stats" "graphics" "grDevices"
## [9] "utils" "datasets" "methods" "base"
(library(TInPosition))
## [1] "TInPosition" "TExPosition" "InPosition" "ExPosition"
## [5] "prettyGraphs" "Matrix" "stats" "graphics"
## [9] "grDevices" "utils" "datasets" "methods"
## [13] "base"
(library(PTCA4CATA))
##
## Attaching package: 'PTCA4CATA'
## The following object is masked from 'package:InPosition':
##
## boot.ratio.test
## [1] "PTCA4CATA" "TInPosition" "TExPosition" "InPosition"
## [5] "ExPosition" "prettyGraphs" "Matrix" "stats"
## [9] "graphics" "grDevices" "utils" "datasets"
## [13] "methods" "base"
(library(data4PCCAR))
## [1] "data4PCCAR" "PTCA4CATA" "TInPosition" "TExPosition"
## [5] "InPosition" "ExPosition" "prettyGraphs" "Matrix"
## [9] "stats" "graphics" "grDevices" "utils"
## [13] "datasets" "methods" "base"
(library(dplyr))
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## [1] "dplyr" "data4PCCAR" "PTCA4CATA" "TInPosition"
## [5] "TExPosition" "InPosition" "ExPosition" "prettyGraphs"
## [9] "Matrix" "stats" "graphics" "grDevices"
## [13] "utils" "datasets" "methods" "base"
(library(gridExtra))
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
## [1] "gridExtra" "dplyr" "data4PCCAR" "PTCA4CATA"
## [5] "TInPosition" "TExPosition" "InPosition" "ExPosition"
## [9] "prettyGraphs" "Matrix" "stats" "graphics"
## [13] "grDevices" "utils" "datasets" "methods"
## [17] "base"
(library(grid))
## [1] "grid" "gridExtra" "dplyr" "data4PCCAR"
## [5] "PTCA4CATA" "TInPosition" "TExPosition" "InPosition"
## [9] "ExPosition" "prettyGraphs" "Matrix" "stats"
## [13] "graphics" "grDevices" "utils" "datasets"
## [17] "methods" "base"
datae <- data[,8]
resBADA <- tepBADA(DATA = data1,
scale = 'SS1', center = TRUE,
DESIGN = datae,
make_design_nominal = TRUE,
group.masses = NULL,
weights = NULL, graphs = FALSE)
ScreePlot
PlotScree(ev = resBADA$TExPosition.Data$eigs,
p.ev = NULL, max.ev = NULL, alpha = 0.05,
col.ns = "#006D2C", col.sig = "#54278F",
title = "Explained Variance per Dimension",plotKaiser = TRUE)
HeatMap
color4Var <- prettyGraphs::prettyGraphsColorSelection(ncol(data1))
col <- colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA"))
# Pseudo Heat Map. Correlation ----
# We need correlation to compare with PCA
corrMatBurt.list <- phi2Mat4BurtTable(data1)
corr4MCA.r <- corrplot::corrplot( as.matrix(corrMatBurt.list$phi2.mat), method="color", col=col(200), type="upper", addCoef.col = "black", tl.col = color4Var,
tl.srt = 45, #Text label color and rotation
number.cex = .5,
diag = TRUE )
Fk <- resBADA$TExPosition.Data$fi
Fi <- resBADA$TExPosition.Data$fii
Fj <- resBADA$TExPosition.Data$fj
Factor Map J
col4Var <- prettyGraphsColorSelection(NCOL(data1))
baseMap.j <- PTCA4CATA::createFactorMap(Fj,
col.points = col4Var,
alpha.points = .3,
col.labels = col4Var)
# A graph for the J-set
aggMap.j <- baseMap.j$zeMap_background + # background layer
baseMap.j$zeMap_dots + baseMap.j$zeMap_text # dots & labels
# We print this Map with the following code
dev.new()
print(aggMap.j)
zeLines <- ggplot2::annotate("segment", x = c(0), y = c(0),
xend = Fj[,1],
yend = Fj[,2],
color = col4Var,
alpha = .5,
arrow = arrow(length = unit(.3, "cm") ) )
# Create the map by adding background, labels, and arrows:
aggMap.j.arrows <- baseMap.j$zeMap_background +
zeLines + baseMap.j$zeMap_text
dev.new()
print(aggMap.j.arrows)
Factor Map I
baseMap.i <- PTCA4CATA::createFactorMap(Fi,
col.points = resBADA$Plotting.Data$fii.col,
alpha.points = .3)
# Plain map with color for the I-set
aggMap.i <- baseMap.i$zeMap_background + baseMap.i$zeMap_dots
#---------------------------------------------------------------------
# print this Map
dev.new()
print(aggMap.i)
col4data <- resBADA$Plotting.Data$fii.col
col4Means <- unique(col4data)
# create the map for the means
MapGroup <- PTCA4CATA::createFactorMap(Fk,
axis1 = 1, axis2 = 2,
constraints = baseMap.i$constraints,
title = NULL,
col.points = col4Means,
display.points = TRUE,
pch = 19, cex = 5,
display.labels = TRUE,
col.labels = col4Means,
text.cex = 4,
font.face = "bold",
font.family = "sans",
col.axes = "darkorchid",
alpha.axes = 0.2,
width.axes = 1.1,
col.background = adjustcolor("lavender",
alpha.f = 0.2),
force = 1, segment.size = 0)
# The map with observations and group means
aggMap.i.withMeans <- aggMap.i+
MapGroup$zeMap_dots + MapGroup$zeMap_text
#---------------------------------------------------------------------
# plot it!
dev.new()
print(aggMap.i.withMeans)
Create 75% Tolerance interval polygons
GraphTI.Hull.90 <- MakeToleranceIntervals(Fi,
as.factor(datae),
names.of.factors = c("Dim1","Dim2"),
col = unique(col4data),
line.size = .5, line.type = 3,
alpha.ellipse = .2,
alpha.line = .4,
p.level = .75, # 75% TI
type = 'hull' #
# use 'hull' for convex hull
)
#---------------------------------------------------------------------
# Create the map
aggMap.i.withHull <- aggMap.i +
GraphTI.Hull.90 + MapGroup$zeMap_dots +
MapGroup$zeMap_text + MapGroup$zeMap_dots
#---------------------------------------------------------------------
# Plot it!
dev.new()
print(aggMap.i.withHull)
Inferences
resBADA.inf <- tepBADA.inference.battery(DATA = data1,
scale = 'SS1', center = TRUE,
DESIGN = datae,
make_design_nominal = TRUE,
group.masses = NULL,
weights = NULL,
graphs = FALSE,
k = 2,
test.iters = 100,
critical.value = 2)
## [1] "It is estimated that your iterations will take 0.71 minutes."
## [1] "R is not in interactive() mode. Resample-based tests will be conducted. Please take note of the progress bar."
## ===========================================================================
#---------------------------------------------------------------------
# Confusion matrices
# To be saved as table
fixedCM <- resBADA.inf$Inference.Data$loo.data$fixed.confuse
looedCM <- resBADA.inf$Inference.Data$loo.data$loo.confuse
#---------------------------------------------------------------------
# Create Confidence Interval Plots
BootCube <- resBADA.inf$Inference.Data$boot.data$fi.boot.data$boots
dimnames(BootCube)[[2]] <- c("Dimension 1","Dimension 2")
# use function MakeCIEllipses from package PTCA4CATA
GraphElli <- MakeCIEllipses(BootCube[,1:2,],
names.of.factors = c("Dimension 1","Dimension 2"),
col = col4Means,
p.level = .95
)
#---------------------------------------------------------------------
# create the I-map with Observations, means and confidence intervals
#
aggMap.i.withCI <- aggMap.i + GraphElli + MapGroup$zeMap_text
#---------------------------------------------------------------------
# plot it!
dev.new()
print(aggMap.i.withCI)
Contribution
signed.ctrJ1 <- resBADA$TExPosition.Data$cj * sign(resBADA$TExPosition.Data$fj)
b003.ctrJ.s.11 <- PrettyBarPlot2(signed.ctrJ1[,1],
threshold = 1 / NROW(signed.ctrJ1),
font.size = 5,
# color4bar = gplots::col2hex(col4J.ibm), # we need hex code
main = 'BADA on the IBM-No-Attririon data Set: Variable Contributions (Signed)',
ylab = 'Contributions',
ylim = c(1.2*min(signed.ctrJ1), 1.2*max(signed.ctrJ1))
)
print(b003.ctrJ.s.11)
b004.ctrJ.s.21 <- PrettyBarPlot2(signed.ctrJ1[,2],
threshold = 1 / NROW(signed.ctrJ1),
font.size = 5,
# color4bar = gplots::col2hex(col4J.ibm), # we need hex code
main = 'BADA on the IBM-No-Attririon dataSet: Variable Contributions (Signed)',
ylab = 'Contributions',
ylim = c(1.2*min(signed.ctrJ1), 1.2*max(signed.ctrJ1))
)
print(b004.ctrJ.s.21)
Bootstrap Ratios
BR1 <- resBADA.inf$Inference.Data$boot.data$fj.boot.data$tests$boot.ratios
laDim = 1
ba001.BR11 <- PrettyBarPlot2(BR1[,laDim],
threshold = 2,
font.size = 5,
#color4bar = gplots::col2hex(col4J.ibm),
main = paste0( 'BADA on the IBM-NoAttrition data Set: Bootstrap ratio ',laDim),
ylab = 'Bootstrap ratios'
#ylim = c(1.2*min(BR[,laDim]), 1.2*max(BR[,laDim]))
)
print(ba001.BR11)
#
laDim = 2
ba002.BR21 <- PrettyBarPlot2(BR1[,laDim],
threshold = 2,
font.size = 5,
#color4bar = gplots::col2hex(col4J.ibm),
main = paste0(
'BADA on the IBM-NoAttrition data Set: Bootstrap ratio ',laDim),
ylab = 'Bootstrap ratios'
)
print(ba002.BR21)
DiCA
data2 <- data[,11:32]
data2$Age <- data$Age
data2$MonthlyIncome <- cut(data1$MonthlyIncome,breaks = c(min(data2$MonthlyIncome)-1,5000 ,10000, max(data2$MonthlyIncome)+1),labels = c(1,2,3))
data2$DailyRate <- cut(data2$DailyRate,breaks = c(min(data2$DailyRate)-1,600 ,1100, max(data1$DailyRate)+1),labels = c(1,2,3))
data2$HourlyRate <- cut(data2$HourlyRate,breaks = c(min(data2$HourlyRate)-1,55 ,80, max(data2$HourlyRate)+1),labels = c(1,2,3))
data2$MonthlyRate <- cut(data2$MonthlyRate,breaks = c(min(data2$MonthlyRate)-1,10000 ,17500, max(data2$MonthlyRate)+1),labels = c(1,2,3))
data2$DistanceFromHome <- cut(data2$DistanceFromHome,breaks = c(min(data2$DistanceFromHome)-1,6 , max(data2$DistanceFromHome)+1),labels = c(1,2))
data2$Education <- cut(data2$Education,breaks = c(min(data2$Education)-1, 2,3 , max(data2$Education)+1),labels = c(1,2,3))
data2$JobInvolvement <- cut(data2$JobInvolvement,breaks = c(min(data2$JobInvolvement)-1,2.5,3.5, max(data2$JobInvolvement)+1),labels = c(1,2,3))
data2$JobLevel <- cut(data2$JobLevel,breaks = c(min(data2$JobLevel)-1,2,4, max(data2$JobLevel)+1),labels = c(1,2,3))
data2$NumCompaniesWorked <- cut(data2$NumCompaniesWorked,breaks = c(min(data2$NumCompaniesWorked)-1, 2,6, max(data2$NumCompaniesWorked)+1),labels = c(1,2,3))
data2$TotalWorkingYears <- cut(data2$TotalWorkingYears,breaks = c(min(data2$TotalWorkingYears)-1, 10, max(data2$TotalWorkingYears)+1),labels = c(1,2))
data2$PercentSalaryHike <- cut(data2$PercentSalaryHike,breaks = c(min(data2$PercentSalaryHike)-1, 13,19, max(data2$PercentSalaryHike)+1),labels = c(1,2,3))
data2$WorkLifeBalance <- cut(data2$WorkLifeBalance,breaks = c(min(data2$WorkLifeBalance)-1, 2, max(data2$WorkLifeBalance)+1),labels = c(1,2))
data2$Age <- cut(data2$Age,breaks = c(min(data2$Age)-1, 30,40, max(data2$Age)+1),labels = c(1,2,3))
data2$TrainingTimesLastYear <- cut(data2$TrainingTimesLastYear,breaks = c(min(data2$TrainingTimesLastYear)-1, 2,4, max(data2$TrainingTimesLastYear)+1),labels = c(1,2,3))
data2$YearsAtCompany <- cut(data2$YearsAtCompany,breaks = c(min(data2$YearsAtCompany)-1, 5,15, max(data2$YearsAtCompany)+1),labels = c(1,2,3))
data2$YearsInCurrentRole <- cut(data2$YearsInCurrentRole,breaks = c(min(data2$YearsInCurrentRole)-1, 4,10, max(data2$YearsInCurrentRole)+1),labels = c(1,2,3))
data2$YearsSinceLastPromotion <- cut(data2$YearsSinceLastPromotion,breaks = c(min(data2$YearsSinceLastPromotion)-1, 2,7, max(data2$YearsSinceLastPromotion)+1),labels = c(1,2,3))
data2$YearsWithCurrManager <- cut(data2$YearsWithCurrManager,breaks = c(min(data2$YearsWithCurrManager)-1, 4,10, max(data2$YearsWithCurrManager)+1),labels = c(1,2,3))
data2$PerformanceRating <- as.factor(data2$PerformanceRating)
data2$JobInvolvement <- as.factor(data2$JobInvolvement)
data2$StockOptionLevel <- as.factor(data2$StockOptionLevel)
data2$EnvironmentSatisfaction <- as.factor(data2$EnvironmentSatisfaction)
data2$JobSatisfaction <- as.factor(data2$JobSatisfaction)
data2$RelationshipSatisfaction <- as.factor(data2$RelationshipSatisfaction)
str(data2)
## 'data.frame': 1233 obs. of 23 variables:
## $ MonthlyIncome : Factor w/ 3 levels "1","2","3": 2 1 1 1 1 1 2 2 1 1 ...
## $ DailyRate : Factor w/ 3 levels "1","2","3": 1 3 1 2 3 3 1 3 2 1 ...
## $ HourlyRate : Factor w/ 3 levels "1","2","3": 2 2 1 2 3 2 1 3 3 1 ...
## $ MonthlyRate : Factor w/ 3 levels "1","2","3": 3 3 2 2 1 2 1 2 2 2 ...
## $ DistanceFromHome : Factor w/ 2 levels "1","2": 2 1 1 1 1 2 2 2 2 2 ...
## $ PerformanceRating : Factor w/ 2 levels "3","4": 2 1 1 1 2 2 2 1 1 1 ...
## $ Education : Factor w/ 3 levels "1","2","3": 1 3 1 1 2 1 2 2 2 1 ...
## $ JobInvolvement : Factor w/ 3 levels "1","2","3": 1 2 2 2 3 2 1 2 3 1 ...
## $ JobLevel : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 2 1 1 1 ...
## $ StockOptionLevel : Factor w/ 4 levels "0","1","2","3": 2 1 2 1 4 2 1 3 2 1 ...
## $ NumCompaniesWorked : Factor w/ 3 levels "1","2","3": 1 1 3 1 2 1 1 2 1 1 ...
## $ PercentSalaryHike : Factor w/ 3 levels "1","2","3": 3 1 1 1 3 3 3 1 1 1 ...
## $ TotalWorkingYears : Factor w/ 2 levels "1","2": 1 1 1 1 2 1 1 2 1 1 ...
## $ TrainingTimesLastYear : Factor w/ 3 levels "1","2","3": 2 2 2 1 2 1 1 2 3 2 ...
## $ WorkLifeBalance : Factor w/ 2 levels "1","2": 2 2 2 1 1 2 2 1 2 2 ...
## $ YearsAtCompany : Factor w/ 3 levels "1","2","3": 2 2 1 2 1 1 2 2 1 2 ...
## $ YearsInCurrentRole : Factor w/ 3 levels "1","2","3": 2 2 1 2 1 1 2 2 1 2 ...
## $ YearsSinceLastPromotion : Factor w/ 3 levels "1","2","3": 1 2 1 2 1 1 1 2 1 1 ...
## $ YearsWithCurrManager : Factor w/ 3 levels "1","2","3": 2 1 1 2 1 1 2 2 1 2 ...
## $ EnvironmentSatisfaction : Factor w/ 4 levels "1","2","3","4": 3 4 1 4 3 4 4 3 1 4 ...
## $ JobSatisfaction : Factor w/ 4 levels "1","2","3","4": 2 3 2 4 1 3 3 3 2 3 ...
## $ RelationshipSatisfaction: Factor w/ 4 levels "1","2","3","4": 4 3 4 3 1 2 2 2 3 4 ...
## $ Age : Factor w/ 3 levels "1","2","3": 3 2 1 2 3 1 2 2 2 1 ...
HeatMap
color4Var1 <- prettyGraphs::prettyGraphsColorSelection(ncol(data2))
col <- colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA"))
# Pseudo Heat Map. Correlation ----
# We need correlation to compare with PCA
corrMatBurt.list1 <- phi2Mat4BurtTable(data2)
corr4MCA.r1 <- corrplot::corrplot( as.matrix(corrMatBurt.list1$phi2.mat), method="color", col=col(200), type="upper", addCoef.col = "black", tl.col = color4Var1,
tl.srt = 45, #Text label color and rotation
number.cex = .5,
diag = TRUE )
resDICA <- tepDICA(DATA = data2,
DESIGN = datae,
make_design_nominal = TRUE,
make_data_nominal = TRUE,
group.masses = NULL,
weights = NULL, graphs = FALSE)
ScreePlot for DICA
resDICA.inf1 <- tepDICA.inference.battery(DATA = data2,
DESIGN = datae,
make_design_nominal = TRUE,
make_data_nominal = TRUE,
group.masses = NULL,
weights = NULL,
graphs = FALSE,
k = 2,
test.iters = 100,
critical.value = 2)
## [1] "It is estimated that your iterations will take 0.45 minutes."
## [1] "R is not in interactive() mode. Resample-based tests will be conducted. Please take note of the progress bar."
## ===========================================================================
PlotScree(ev = resDICA$TExPosition.Data$eigs,
p.ev = resDICA.inf1$Inference.Data$components$p.vals, max.ev = NULL, alpha = 0.05,
col.ns = "#006D2C", col.sig = "#54278F",
title = "Explained Variance per Dimension",plotKaiser = TRUE)
Factor Map J
#col4Var1 <- prettyGraphsColorSelection(NCOL(data))
baseMap.j1 <- PTCA4CATA::createFactorMap(resDICA$TExPosition.Data$fj,
col.points = resDICA$Plotting.Data$fj.col,
alpha.points = .3,
col.labels = resDICA$Plotting.Data$fj.col)
# A graph for the J-set
aggMap.j1 <- baseMap.j1$zeMap_background + # background layer
baseMap.j1$zeMap_dots + baseMap.j1$zeMap_text # dots & labels
# We print this Map with the following code
dev.new()
print(aggMap.j1)
Factor Map I
baseMap.i1 <- PTCA4CATA::createFactorMap(resDICA$TExPosition.Data$fii,
col.points = resDICA$Plotting.Data$fii.col,
alpha.points = .3)
# Plain map with color for the I-set
aggMap.i1 <- baseMap.i1$zeMap_background + baseMap.i1$zeMap_dots
#---------------------------------------------------------------------
# print this Map
dev.new()
print(aggMap.i1)
col4data1 <- resDICA$Plotting.Data$fii.col
col4Means1 <- unique(col4data1)
# create the map for the means
MapGroup1 <- PTCA4CATA::createFactorMap(resDICA$TExPosition.Data$fi,
axis1 = 1, axis2 = 2,
constraints = baseMap.i1$constraints,
title = NULL,
col.points = col4Means1,
display.points = TRUE,
pch = 19, cex = 5,
display.labels = TRUE,
col.labels = col4Means1,
text.cex = 4,
font.face = "bold",
font.family = "sans",
col.axes = "darkorchid",
alpha.axes = 0.2,
width.axes = 1.1,
col.background = adjustcolor("lavender",
alpha.f = 0.2),
force = 1, segment.size = 0)
# The map with observations and group means
aggMap.i.withMeans1 <- aggMap.i1+
MapGroup1$zeMap_dots + MapGroup1$zeMap_text
#---------------------------------------------------------------------
# plot it!
dev.new()
print(aggMap.i.withMeans1)
Create 75% Tolerance interval polygons
GraphTI.Hull.901 <- MakeToleranceIntervals(resDICA$TExPosition.Data$fii,
as.factor(datae),
names.of.factors = c("Dim1","Dim2"),
col = unique(col4data1),
line.size = .5, line.type = 3,
alpha.ellipse = .2,
alpha.line = .4,
p.level = .75, # 75% TI
type = 'hull' #
# use 'hull' for convex hull
)
#---------------------------------------------------------------------
# Create the map
aggMap.i.withHull1 <- aggMap.i1 +
GraphTI.Hull.901 + MapGroup1$zeMap_dots +
MapGroup1$zeMap_text + MapGroup1$zeMap_dots
#---------------------------------------------------------------------
# Plot it!
dev.new()
print(aggMap.i.withHull1)
Inferences
#---------------------------------------------------------------------
# Confusion matrices
# To be saved as table
fixedCM1 <- resDICA.inf1$Inference.Data$loo.data$fixed.confuse
looedCM1 <- resDICA.inf1$Inference.Data$loo.data$loo.confuse
#---------------------------------------------------------------------
# Create Confidence Interval Plots
BootCube1 <- resDICA.inf1$Inference.Data$boot.data$fi.boot.data$boots
dimnames(BootCube1)[[2]] <- c("Dimension 1","Dimension 2")
# use function MakeCIEllipses from package PTCA4CATA
GraphElli1 <- MakeCIEllipses(BootCube1[,1:2,],
names.of.factors = c("Dimension 1","Dimension 2"),
col = col4Means1,
p.level = .95
)
#---------------------------------------------------------------------
# create the I-map with Observations, means and confidence intervals
#
aggMap.i.withCI1 <- aggMap.i1 + GraphElli1 + MapGroup1$zeMap_text
#---------------------------------------------------------------------
# plot it!
dev.new()
print(aggMap.i.withCI1)
Contribution
signed.ctrJ <- resDICA$TExPosition.Data$cj * sign(resDICA$TExPosition.Data$fj)
b003.ctrJ.s.1 <- PrettyBarPlot2(signed.ctrJ[,1],
threshold = 1 / NROW(signed.ctrJ),
font.size = 5,
# color4bar = gplots::col2hex(col4J.ibm), # we need hex code
main = 'DICA on the IBM-No-Attririon data Set: Variable Contributions (Signed)',
ylab = 'Contributions',
ylim = c(1.2*min(signed.ctrJ), 1.2*max(signed.ctrJ))
)
print(b003.ctrJ.s.1)
b004.ctrJ.s.2 <- PrettyBarPlot2(signed.ctrJ[,2],
threshold = 1 / NROW(signed.ctrJ),
font.size = 5,
# color4bar = gplots::col2hex(col4J.ibm), # we need hex code
main = 'DICA on the IBM-No-Attririon dataSet: Variable Contributions (Signed)',
ylab = 'Contributions',
ylim = c(1.2*min(signed.ctrJ), 1.2*max(signed.ctrJ))
)
print(b004.ctrJ.s.2)
Bootstrap Ratios
BR <- resDICA.inf1$Inference.Data$boot.data$fj.boot.data$tests$boot.ratios
laDim = 1
ba001.BR1 <- PrettyBarPlot2(BR[,laDim],
threshold = 2,
font.size = 5,
#color4bar = gplots::col2hex(col4J.ibm),
main = paste0( 'DICA on the IBM-NoAttrition data Set: Bootstrap ratio ',laDim),
ylab = 'Bootstrap ratios'
#ylim = c(1.2*min(BR[,laDim]), 1.2*max(BR[,laDim]))
)
print(ba001.BR1)
#
laDim = 2
ba002.BR2 <- PrettyBarPlot2(BR[,laDim],
threshold = 2,
font.size = 5,
#color4bar = gplots::col2hex(col4J.ibm),
main = paste0(
'DICA on the IBM-NoAttrition data Set: Bootstrap ratio ',laDim),
ylab = 'Bootstrap ratios'
)
print(ba002.BR2)
Conclusion